Feasibility of Using Near-Infrared Spectroscopy for Rapid Quantification of 17β-Estradiol Sorption Coefficients in Soil
Bibliographic record
Abstract
Livestock manure contains natural steroid hormones, with the most potent being 17β-estradiol. The transport of steroid hormones from agricultural fields to adjacent water bodies can result in 17β-estradiol environmental contamination impacting aquatic organisms. Sorption coefficients are useful input into models that estimate risk of water contamination. The feasibility of applying near-infrared spectroscopy (NIRS) for determining sorption coefficients of 17β-estradiol in soil was investigated for two irregular undulating to hummocky terrain landscapes in Manitoba and Saskatchewan, Canada. A total of 609 soil samples in 140 soil profiles were collected from several horizons to a depth of 1 m. Air-dried and sieved (2 mm) soil samples were analyzed for soil organic carbon (SOC), soil pH, and soil texture. Sorption coefficients of 17β-estradiol were determined by a batch equilibrium process. Spectral data were collected from soil samples (25 g) using two instruments, the 45VISNIR Zeiss Corona (wavelength range 700-1690 nm) and the Foss NIRSystems 6500 (wavelength range 1100-2500 nm). Regardless of the site and instrument, the predictive models were excellent for both SOC and 17β-estradiol sorption coefficients. The data thus generated can be used as input parameters in fate models for efficient risk assessments and decision-making programs for environmental safety where soils are at risk of receiving inputs of 17β-estradiol. Calibration results for soil pH were also adequate with Corona outperforming the Foss instrument. Soil texture predictions were relatively unsuccessful regardless of the instrument and site.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".